International Journal of Data Warehousing and Mining

Aims & Scope

The International Journal of Data Warehousing and Mining (IJDWM) aims to publish and deliver knowledge in the areas of data warehousing and data mining on an international basis.

This journal is published on a quarterly basis and is targeted at both academic researchers and practicing IT professionals as it is devoted to the publications of high-quality papers on theoretical developments and practical applications in data warehousing and data mining.

Original research papers, state-of-the-art reviews, and technical notes are invited for publication.

The journal accepts paper submission of any work relevant to data warehousing and data mining with special attention to papers focusing on mining of data from data warehouses, integration of databases, data warehousing, data mining, and holistic approaches to mining and archiving data.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	20346

Journal Citation Indicator

Year	Value
2024	117

Impact Factor

Year	Value
2024	0.50

SJR (SCImago Journal Rank)

Year	Value
2024	0.215

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	26

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Multi-Label Classification

  Citation: 1825

  Authors: Grigorios, Ioannis

• A Survey of Extract–Transform–Load Technology

  Citation: 186

  Authors: Panos

• Fusion Cubes

  Citation: 80

  Authors: Alberto, Jérôme, Lorena, Matteo, Jose-Norberto, Felix, Torben, Stefano Bach, Juan, Panos, Gottfried

• Data Field for Hierarchical Clustering

  Citation: 74

  Authors: Shuliang, Wenyan, Deyi, Deren

• Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces

  Citation: 72

  Authors: Baoxun, Joshua Zhexue, Graham, Qiang, Yunming

• A Survey of Multidimensional Modeling Methodologies

  Citation: 63

  Authors: Oscar, Alberto

• BAHUI

  Citation: 54

  Authors: Wei, Yu, Jinhong

• A Survey on Temporal Data Warehousing

  Citation: 50

  Authors: Matteo, Stefano

• Mining Association Rules in Data Warehouses

  Citation: 49

  Authors: Haorianto Cokrowijoyo, David

• A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem

  Citation: 42

  Authors: Debashree, Suyel, Seifedine

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